{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e34d63c6",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb388092",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import json\n",
    "\n",
    "from transformers import MarianTokenizer, AutoTokenizer\n",
    "\n",
    "# from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39063baa",
   "metadata": {},
   "source": [
    "# Defining plot styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9965d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr, kde, gaussian_kde\n",
    "import matplotlib\n",
    "from matplotlib import rc\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d57619a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['text.usetex'] = True\n",
    "#plt.rcParams['text.usetex'] = False\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = ['Times New Roman']\n",
    "\n",
    "# plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 26\n",
    "plt.rcParams['axes.labelsize'] = 26\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rcParams['axes.titlesize'] = 26\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "plt.rcParams['legend.fontsize'] = 18\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebef0624",
   "metadata": {},
   "source": [
    "# Load / Process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8fd0ad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lnet/work/people/limisiewicz/mt-tokenizer-bias/.virtualenv/lib/python3.10/site-packages/transformers/models/marian/tokenization_marian.py:194: UserWarning: Recommended: pip install sacremoses.\n",
      "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
     ]
    }
   ],
   "source": [
    "tokenizer_de = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-de\")\n",
    "tokenizer_he = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-he\")\n",
    "tokenizer_es = MarianTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-es\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94ffd1da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_num_of_tokens_per_gender(professions, translations_dict, tokenizer, target_file):\n",
    "    male_count, male_tokens, female_count, female_tokens = 0, 0, 0, 0\n",
    "    male_num_of_tokens_map, female_num_of_tokens_map = {}, {}\n",
    "    for profession in professions:\n",
    "        m, f = list(translations_dict[profession]['Male']), list(translations_dict[profession]['Female'])\n",
    "        \n",
    "        for mp in m:\n",
    "            male_count += 1\n",
    "            with tokenizer.as_target_tokenizer():\n",
    "                male_num_of_tokens = len(tokenizer(mp)['input_ids'][:-1])\n",
    "            male_tokens += male_num_of_tokens\n",
    "            if male_num_of_tokens not in male_num_of_tokens_map:\n",
    "                male_num_of_tokens_map[male_num_of_tokens] = 1\n",
    "            else:\n",
    "                male_num_of_tokens_map[male_num_of_tokens] += 1\n",
    "                \n",
    "        for fp in f:\n",
    "            female_count += 1\n",
    "            with tokenizer.as_target_tokenizer():\n",
    "                female_num_of_tokens = len(tokenizer(fp)['input_ids'][:-1])\n",
    "            female_tokens += female_num_of_tokens\n",
    "            if female_num_of_tokens not in female_num_of_tokens_map:\n",
    "                female_num_of_tokens_map[female_num_of_tokens] = 1\n",
    "            else:\n",
    "                female_num_of_tokens_map[female_num_of_tokens] += 1\n",
    "    with open(target_file, 'w+') as f:\n",
    "        f.write(\"male: \" + str(male_tokens / male_count) + \"\\n\")\n",
    "        f.write(\"female: \" + str(female_tokens / female_count) + \"\\n\")\n",
    "    max_tokens = max(max(male_num_of_tokens_map.keys()),max(female_num_of_tokens_map.keys()))\n",
    "    return male_tokens / male_count, female_tokens / female_count, male_num_of_tokens_map, female_num_of_tokens_map, max_tokens\n",
    "\n",
    "def count_tokens(translations_dict, mp, count,tokenizer, tokens, num_of_tokens_map, gender):\n",
    "    professions = list(translations_dict[mp][gender])\n",
    "    count += len(professions)\n",
    "    for p in professions:\n",
    "        with tokenizer.as_target_tokenizer():\n",
    "            num_of_tokens = len(tokenizer(p)['input_ids'][:-1])\n",
    "        tokens += num_of_tokens\n",
    "        if num_of_tokens not in num_of_tokens_map:\n",
    "            num_of_tokens_map[num_of_tokens] = 1\n",
    "        else:\n",
    "            num_of_tokens_map[num_of_tokens] += 1\n",
    "    return tokens,count\n",
    "\n",
    "def get_annotations_dict(merged_annotations):\n",
    "    translations_dict = {}\n",
    "    professions = set()\n",
    "    with open(merged_annotations, 'r') as f:\n",
    "        lines = f.readlines()\n",
    "    for line in lines:\n",
    "        line = line.strip()\n",
    "        columns = line.split(\"\\t\")\n",
    "        english_profession = columns[0]\n",
    "        professions.add(english_profession)\n",
    "        if not english_profession in translations_dict:\n",
    "            translations_dict[english_profession] = {'Male': [], 'Female': []}\n",
    "        for i in range(1, len(columns)):\n",
    "            if columns[i] != \"\":\n",
    "                if i % 2 and columns[i]:\n",
    "                    translations_dict[english_profession]['Male'].append(columns[i])\n",
    "                else:\n",
    "                    translations_dict[english_profession]['Female'].append(columns[i])\n",
    "    return translations_dict, professions\n",
    "\n",
    "def get_num_of_tokens_per_stereotype(male_stereotype,female_stereotype,translations_dict, tokenizer, target_file):\n",
    "    stereotype_count, stereotype_tokens, anti_stereotype_count, anti_stereotype_tokens = 0, 0, 0, 0\n",
    "    stereotype_num_of_tokens_map, anti_stereotype_num_of_tokens_map = {}, {}\n",
    "    with tokenizer.as_target_tokenizer():\n",
    "        for mp in male_stereotype:\n",
    "            tokens,count = count_tokens(translations_dict, mp, stereotype_count,tokenizer, stereotype_tokens, stereotype_num_of_tokens_map,'Male')\n",
    "            stereotype_tokens+=tokens\n",
    "            stereotype_count+=count\n",
    "            tokens,count = count_tokens(translations_dict, mp, anti_stereotype_count,tokenizer, anti_stereotype_tokens, anti_stereotype_num_of_tokens_map,'Female')\n",
    "            anti_stereotype_tokens += tokens\n",
    "            anti_stereotype_count += count\n",
    "        for fp in female_stereotype:\n",
    "            tokens,count = count_tokens(translations_dict, fp, stereotype_count,tokenizer, stereotype_tokens, stereotype_num_of_tokens_map,'Female')\n",
    "            stereotype_tokens += tokens\n",
    "            stereotype_count += count\n",
    "            tokens,count = count_tokens(translations_dict, fp, anti_stereotype_count,tokenizer, anti_stereotype_tokens, anti_stereotype_num_of_tokens_map,'Male')\n",
    "            anti_stereotype_tokens += tokens\n",
    "            anti_stereotype_count += count\n",
    "    with open(target_file, 'w+') as f:\n",
    "        f.write(\"stereotype: \" + str(stereotype_tokens / stereotype_count) + \"\\n\")\n",
    "        f.write(\"anti stereotype: \" + str(anti_stereotype_tokens / anti_stereotype_count) + \"\\n\")\n",
    "    max_tokens = max(max(stereotype_num_of_tokens_map.keys()),max(anti_stereotype_num_of_tokens_map.keys()))\n",
    "    return stereotype_tokens / stereotype_count, anti_stereotype_tokens / anti_stereotype_count,\\\n",
    "           stereotype_num_of_tokens_map, anti_stereotype_num_of_tokens_map, max_tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "619ed6cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lnet/work/people/limisiewicz/mt-tokenizer-bias/.virtualenv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:3578: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "he_translations, professions = get_annotations_dict(\"../data/he_merged_translations_new.txt\")\n",
    "de_translations, professions = get_annotations_dict(\"../data/de_merged_translations_new.txt\")\n",
    "he_male_average_tokens, he_female_average_tokens, he_male_num_of_tokens_map, he_female_num_of_tokens_map, he_max_tokens = \\\n",
    "        get_num_of_tokens_per_gender(professions, he_translations, tokenizer_he, \"../data/he_tokens_per_gender.txt\")\n",
    "de_male_average_tokens, de_female_average_tokens, de_male_num_of_tokens_map, de_female_num_of_tokens_map, de_max_tokens = \\\n",
    "    get_num_of_tokens_per_gender(professions, de_translations, tokenizer_de, \"../data/de_tokens_per_gender.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bea8561",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_translations, professions = get_annotations_dict(\"../data/es_merged_translations_new.txt\")\n",
    "es_male_average_tokens, de_female_average_tokens, es_male_num_of_tokens_map, es_female_num_of_tokens_map, es_max_tokens = \\\n",
    "    get_num_of_tokens_per_gender(professions, es_translations, tokenizer_es, \"../data/es_tokens_per_gender.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "030df156",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../data/male_stereotype\",\"r\") as f:\n",
    "    male_stereotype = f.readlines()\n",
    "    male_stereotype = [i.strip() for i in male_stereotype]\n",
    "with open(\"../data/female_stereotype\",\"r\") as f:\n",
    "    female_stereotype = f.readlines()\n",
    "    female_stereotype = [i.strip() for i in female_stereotype]\n",
    "\n",
    "\n",
    "he_stereotype_avg_tokens, he_anti_stereotype_avg_tokens, \\\n",
    "he_stereotype_num_of_tokens_map, he_anti_stereotype_num_of_tokens_map, he_max_tokens = \\\n",
    "    get_num_of_tokens_per_stereotype(male_stereotype, female_stereotype, he_translations, tokenizer_he, \"../data/he_tokens_per_stereotype.txt\")\n",
    "\n",
    "de_stereotype_avg_tokens, de_anti_stereotype_avg_tokens, \\\n",
    "de_stereotype_num_of_tokens_map, de_anti_stereotype_num_of_tokens_map, de_max_tokens = \\\n",
    "    get_num_of_tokens_per_stereotype(male_stereotype, female_stereotype, de_translations, tokenizer_de, \"../data/de_tokens_per_stereotype.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d108ee11",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_stereotype_avg_tokens, es_anti_stereotype_avg_tokens, \\\n",
    "es_stereotype_num_of_tokens_map, es_anti_stereotype_num_of_tokens_map, es_max_tokens = \\\n",
    "    get_num_of_tokens_per_stereotype(male_stereotype, female_stereotype, es_translations, tokenizer_es, \"../data/es_tokens_per_stereotype.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7f253d",
   "metadata": {},
   "source": [
    "# Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17826b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphs_3_and_4(group1_num_of_tokens_map, group2_num_of_tokens_map, max_tokens, title, group1_name,group2_name):\n",
    "    for i in range(1,max_tokens+1):\n",
    "        if i not in group1_num_of_tokens_map:\n",
    "            group1_num_of_tokens_map[i] = 0\n",
    "        if i not in group2_num_of_tokens_map:\n",
    "            group2_num_of_tokens_map[i] = 0\n",
    "    group1_num_of_tokens_map = collections.OrderedDict(sorted(group1_num_of_tokens_map.items()))\n",
    "    group2_num_of_tokens_map = collections.OrderedDict(sorted(group2_num_of_tokens_map.items()))\n",
    "\n",
    "\n",
    "    \n",
    "    if 'Gender' in title:\n",
    "        graph_num = '3'\n",
    "        data_1 = pd.DataFrame({'tokens' : group1_num_of_tokens_map.keys(),\n",
    "                              'count': group1_num_of_tokens_map.values(),\n",
    "                              'gender': ['male'] * len(group1_num_of_tokens_map)})\n",
    "        data_2 = pd.DataFrame({'tokens' : group2_num_of_tokens_map.keys(),\n",
    "                              'count': group2_num_of_tokens_map.values(),\n",
    "                              'gender': ['female'] * len(group2_num_of_tokens_map)})\n",
    "\n",
    "        data= pd.concat([data_1, data_2])\n",
    "                                  \n",
    "        g = sns.catplot(x=\"tokens\", y=\"count\",\n",
    "                data=data, kind=\"bar\", hue='gender', \n",
    "                hue_order = ['male', 'female'],\n",
    "                palette=sns.color_palette(['lightblue', 'brown']),\n",
    "                height=4.5, aspect=1.8, legend=False)\n",
    "        leg_title = 'gender'\n",
    "        fig = g.fig\n",
    "        axes = g.axes\n",
    "        \n",
    "    else:\n",
    "        graph_num = '4'\n",
    "        data_1 = pd.DataFrame({'tokens' : group1_num_of_tokens_map.keys(),\n",
    "                              'count': group1_num_of_tokens_map.values(),\n",
    "                              'stereotype': ['pro'] * len(group1_num_of_tokens_map)})\n",
    "        data_2 = pd.DataFrame({'tokens' : group2_num_of_tokens_map.keys(),\n",
    "                              'count': group2_num_of_tokens_map.values(),\n",
    "                              'stereotype': ['anti'] * len(group2_num_of_tokens_map)})\n",
    "\n",
    "        data = pd.concat([data_1, data_2])\n",
    "          \n",
    "        g = sns.catplot(x=\"tokens\", y='count',\n",
    "                data=data, kind=\"bar\", hue='stereotype', \n",
    "                hue_order = ['pro', 'anti'],\n",
    "                palette=sns.color_palette(['mediumaquamarine', 'darkorange']),\n",
    "                height=4.5, aspect=1.8, legend=False)\n",
    "        leg_title = 'stereotype'\n",
    "        fig = g.fig\n",
    "        axes = g.axes\n",
    "\n",
    "        \n",
    "    axes[0,0].set_xlim(left=-0.5, right=3.5)\n",
    "    axes[0,0].set_ylim(bottom=0.0, top=40)\n",
    "        \n",
    "    if 'Hebrew' in title:\n",
    "        lang = 'he'\n",
    "        axes[0,0].legend(ncol=1,loc='lower right', bbox_to_anchor=(.99, 0.4), title=leg_title)\n",
    "    elif \"German\" in title:\n",
    "        lang = 'de'\n",
    "    elif \"Spanish\" in title:\n",
    "        lang = 'es'\n",
    "    else:\n",
    "        raise ValueError(f\"Unspupported language.\")\n",
    "        \n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(f'../graphs/graph_{graph_num}_{lang}.pdf', dpi=300)\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b51705a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 36, 2: 21, 3: 11}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_male_num_of_tokens_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2c9bf5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 17, 1: 20, 3: 7, 4: 2}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "es_male_num_of_tokens_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50e676d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 41, 1: 6, 3: 12, 4: 8, 5: 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de_female_num_of_tokens_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "152e1262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(de_male_num_of_tokens_map, de_female_num_of_tokens_map, de_max_tokens, \n",
    "               \"German num of tokens per Gender\", \"Male\", \"Female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "acf3ed0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(he_male_num_of_tokens_map, he_female_num_of_tokens_map, he_max_tokens, \n",
    "               \"Hebrew num of tokens per Gender\", \"Male\", \"Female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad586cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(es_male_num_of_tokens_map, es_female_num_of_tokens_map, es_max_tokens, \n",
    "               \"Spanish num of tokens per Gender\", \"Male\", \"Female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f807985f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(he_stereotype_num_of_tokens_map, he_anti_stereotype_num_of_tokens_map, he_max_tokens, \"Hebrew num of tokens per stereotype\", \"stereotype\", \"Anti stereotype\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b6ae468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(de_stereotype_num_of_tokens_map, de_anti_stereotype_num_of_tokens_map, de_max_tokens, \"German num of tokens per stereotype\", \"stereotype\", \"Anti stereotype\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fb8d742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphs_3_and_4(es_stereotype_num_of_tokens_map, es_anti_stereotype_num_of_tokens_map, es_max_tokens, \"Spanish num of tokens per stereotype\", \"stereotype\", \"Anti stereotype\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
